Efficient Data Chucking Approach To Optimizing Information Leakage In Cloud Computing

Abstract

This paper presents the Rapid Asymmetric Maximum Algorithm, an optimized version of the AE (Asymmetric Extremum) algorithm, designed to reduce computational load and enhance resistance to byte-shifting in data chunking. In the Rapid Asymmetric Maximum Algorithm (AE), for example, two types of windows—fixed and variable-sized—are used, but they are arranged differently. The byte containing the maximum value is placed at the start of a chunk, followed by a variable-sized window and a fixed-sized window. After determining which byte in the fixed-sized window has the highest value, the algorithm looks to see if the next byte has a greater value. If a higher value is found, it becomes the new maximum, determining the cut-point. The Rapid Asymmetric Maximum Algorithm increases processing speed by scanning only bytes that are equal to or more than the current maximum value, in contrast to AE. Since bytes are more likely to be smaller than the maximum, fewer checks are needed, reducing overhead. The algorithm's sliding window method uses a hash to identify the pattern and begins at the beginning of the chunk and moves leftward until it is identified. This method shares similarities with Rabin-based chunking, as it uses fixed windows and interspersed bytes to determine the cut-point.